The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
Autonomous robots have long been the stuff of science fiction fantasy. One technical challenge for autonomous robots is the need to be able to identify where they are, where they have been and plan where they are going. Traditional SLAM techniques have improved significantly in recent years; however, there remain considerable technical challenges to providing fast accurate and reliable positional awareness to robots and self-guiding mobile platforms. One especially challenging area involves recognizing the autonomous robot location and obstructions near the autonomous robot accurately and quickly. A variety of different approaches has been tried. For example, RFID/Wi-Fi approaches have proven to be expensive and of limited accuracy. Depth sensor based approaches have been found to be high cost and suffering from power drain and interference issues. Marker-based approaches require markers placed within the work area—limiting the useful area in which the device can operate. Visual approaches currently are slow, leading to failure when used in fast motion applications. Visual approaches can also suffer scale ambiguity.
In recent years, there has been enormous interest in making vehicles smarter. Autonomous vehicles could free drivers of the burden of driving while enhancing vehicle safety. Autonomous vehicles can benefit from fast, accurate and reliable positional awareness. For example, for an autonomous vehicle to follow a road, it needs to know its location on the road, where it has been previously and where it is planning on going. For the autonomous vehicle to stay in a particular lane, it needs to know the location of the lane markers. When an obstacle is detected in the planned path, the planned route needs to be modified by the autonomous vehicle to avoid the obstacle and continue its way to its destination. In general, highways tend to be more predictable and orderly, with road surfaces typically well maintained and lanes well-marked. In contrast, residential or urban driving environments feature a much higher degree of unpredictability with many generic objects, inconsistent lane markings, and elaborate traffic flow patterns. For an autonomous vehicle to stay in a lane, the localization requirements are in the order of decimeters. GPS alone is insufficient and does not meet these requirements. In today's production-grade autonomous vehicles, critical sensors include radar, sonar, and cameras. Long-range vehicle detection typically requires radar, while nearby car detection can be solved with sonar. Radar works reasonably well for detecting vehicles, but has difficulty distinguishing between different metal objects and thus can register false positives on objects such as tin cans, mailbox, etc. Also, radar provides little orientation information and has a higher variance on the lateral position of objects, making the localization difficult on sharp bends. The utility of sonar is both compromised at high speeds and, even at slow speeds, is limited to a working distance of about two meters.
The challenge of providing fast reliable affordable positional awareness to autonomous robots and vehicles heretofore remained largely unsolved.